## R for Medicine and BiologyR is quickly becoming the number one choice for users in the fields of biology, medicine, and bioinformatics as their main means of storing, processing, sharing, and analyzing biomedical data. R for Medicine and Biology is a step-by-step guide through the use of the statistical environment R, as used in a biomedical domain. Ideal for healthcare professionals, scientists, informaticists, and statistical experts, this resource will provide even the novice programmer with the tools necessary to process and analyze their data using the R environment. Introductory chapters guide readers in how to obtain, install, and become familiar with R and provide a clear introduction to the programming language using numerous worked examples. Later chapters outline how R can be used, not just for biomedical data analysis, but also as an environment for the processing, storing, reporting, and sharing of data and results. The remainder of the book explores areas of R application to common domains of biomedical informatics, including imaging, statistical analysis, data mining/modeling, pathology informatics, epidemiology, clinical trials, and metadata usage. R for Medicine and Biology will provide you with a single desk reference for the R environment and its many capabilities. |

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### Contents

1 | |

CHAPTER 2 The R Environment and Packages | 5 |

CHAPTER 3 Basic Fundamentals of R | 11 |

CHAPTER 4 Plotting Data | 31 |

CHAPTER 5 Example Datasets | 43 |

CHAPTER 6 Importing and Exporting Data in R | 53 |

CHAPTER 7 R SQL and Database Connectivity | 63 |

CHAPTER 8 Using R to Build a Biomedical Database in MySQL | 75 |

CHAPTER 14 Survival Analysis | 183 |

CHAPTER 15 Data Mining and Predictive Modeling with R and Weka | 195 |

CHAPTER 16 Surveillance of Infectious Disease | 233 |

CHAPTER 17 Medical Imaging and R | 241 |

CHAPTER 18 Retrieving Public Microarray Datasets | 271 |

CHAPTER 19 Working with Microarray Data | 285 |

CHAPTER 20 Annotating Microarray Gene Lists | 297 |

CHAPTER 21 Array CGH Analysis | 309 |

CHAPTER 9 Creating Heterogeneous Datasets for Analysis in R | 87 |

CHAPTER 10 Descriptive Statistics in R | 105 |

CHAPTER 11 R and Basic Inferential Statistical Analysis | 121 |

CHAPTER 12 Writing Functions in R | 143 |

CHAPTER 13 Multivariate Analysis in R | 149 |

CHAPTER 22 XML for Storing and Sharing Data | 337 |

363 | |

Appendix | 367 |

385 | |

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aCGH addNode(xmlNode("Node addNode(xmlNode("ScoreDistribution Affymetrix ageatdiagnosis algorithm alivestatus apply argument array Bioconductor biomedical data breast cancer calculate chapter chromosome Classified clones cluster coefficient column components contains create data frame data mining decision tree DICOM element error example Figure fMRI fMRI data folder format function called function Code gene genomic grade graphic groups hashcodes.hash_strings hashcodes.patientid header=T her2 Iteration Kappa statistic labels linear lines of code load matrix mean Median methods microarray MySQL Negative Node nodespos normal distribution object output p-value package parameter parse patient database patientid plot PMML polycystic ovary syndrome predicted probe proteinB read read read read.table regression retrieve rows sample ScoreDistribution scores shown in Code specified standard statistical Summary survival t-test text file tissue microarray Trying Trying Trying tumor values variables variance vector visualize voxel Weka window xs:element